Development of microbial communities in biofilm and activated sludge in a hybrid reactor

Microorganisms play a key role in biological wastewater treatment. The form in which biomass develops determines the efficiency and mechanisms of organic compound conversion, due to different conditions in various microbial structures. However, the results of studies comparing the microbial communities in biofilm and activated sludge have often conflicted. Therefore, this study compared the composition and development of the bacterial communities in biofilm and activated sludge in a hybrid reactor, employing 16S rRNA sequencing. Statistical analysis of the sequencing data included the identification of taxa characteristic to the biofilm and activated sludge, alpha and beta diversity analysis, and network analysis. These analyses indicated that the biofilm bacterial community was richer and more diverse than the activated sludge community. The mean numbers of OTU were 1614 in the biofilm and 993 in the activated sludge, and the mean values of the Chao1 (1735 vs. 1105) and Shannon (5.3 vs. 4.3) biodiversity indices were significantly higher for the biofilm. The biofilm was a better environment for development of nitrifiers (e.g., Nitrosomonas, Nitrospira) and phosphorus accumulating organisms (Candidatus Accumulibacter). Bacteria in the biofilm co-occurrence network had more connections (based on Spearman's rank correlation coefficient) with each other, indicating that they interact more than those in the activated sludge.


Results
Bacterial community composition. In order to study the microbial structure of the biofilm and activated sludge that were developing in the IFAS-MBSBBR reactor, a total of 15 samples were taken at intervals during an experiment lasting 573 days. The microbiome of both environments was described at the phylum and genus levels. A total of 26 bacterial phyla and 783 bacterial genera were identified. The most numerous phyla and genera in the biofim and activated sludge samples are presented in in Figs. 1 and 2. Both in the biofilm and the activated sludge, the most numerous phyla were Proteobacteria, with respective mean abundances of 39.3% ± 9.0 and 40.8% ± 8.2, and Bacteroidota, with respective mean abundances of 14.2% ± 4.9 and 26.1% ± 13.7. Additionally, the phylum Chloroflexi was rather abundant in the biofilm (with a mean abundance of 13.9 ± 8.1), while Actinobacteriota and Patescibacteria were relatively abundant in the activated sludge (with mean abundances of 9.0% ± 9.6 and 7.5% ± 8.1, respectively). STAMP analysis identified significant overrepresentations of Chloroflexi, Acidobacteriota, and Nitrospirota in biofilm and of Firmicutes in activated sludge.
In both environments, the abundances of various groups of bacteria changed over time. In the biofilm, the abundance of Proteobacteria and Actinobacteria gradually decreased, while that of Chloroflexi increased. In the activated sludge, the changes in abundance were larger and more rapid, and the abundance of Bacteroidota changed to the largest extent, ranging from 12.7% after 42 days of reactor operation to 52.3% after 110 days, when it was the predominant phylum. The abundance of Patescibacteria also changed substantially: its abundance was highest on the 78th, 205th and 447th days of the process, reaching values of 20.1%, 11.0%, and 7.2%, respectively. Similar changes took place in the abundance of Armatimonadota, which reached 11.4% and 7.6% on the 547th and 573th day, but did not exceed 0.1% in the samples taken at other times. www.nature.com/scientificreports/ At the genus level, the less abundant genera (each < 1.5% of the total bacterial community) combined to constitute the largest shares in all samples of biofilm and activated sludge samples (mean abundance of 45.6% ± 5.8 and 30.5% ± 6.0, respectively). Initially, Ornithinibacter was relatively abundant in the biofilm, which is the reason for its fairly large mean abundance of 4.3% ± 5.3%. Over time, however, the abundance of this group decreased substantially, and at the end of the process, it was only 0.3%. Similarly, the abundance of Rhizorhapis was 13.92% in the first sample, but then it decreased and this genus was not detected after the 205th day. The changes in the abundance of Nitrospira and Candidatus Competibacter are also noteworthy, first increasing and then decreasing. Nitrospira was most abundant in the sample from 447th day (5.7%), and Candidatus Competibacter, in the sample from 110th day (6.4%). The abundance of the remaining genera did not exceed 5% at any time during this study.
In the samples of activated sludge, the abundance of Ornithinibacter also decreased significantly at the beginning of the experiment (from 23.0% in the first sample and 12.5% on the 78th day to values below 3% in subsequent periods). Generally, the abundances of individual genera changed more rapidly in the activated sludge than in the biofilm. There were also rapid decreases and increases in the abundance of many groups of bacteria in the following periods, particularly in the case of uncultured Saccharimonadales and Zoogloea. Figure 3 shows groups of bacteria that differed significantly between biofilm and sludge samples. Denitratisoma, Nitrospira, Candidatus Competibacter, Dechlorosoma, Candidatus Accumulibactrer, and Kouleothrix were significantly more abundant in the biofilm than in the biomass, while Zooglea, uncultured Saccharimonadales, Rhodobacter, and Ottowia were significantly less abundant in the biofilm.
Bacterial diversity. Bacterial community indices were estimated using the EZBioCloud platform ( Table 1).
The average Good's coverage of all samples was 99.75% ± 0.047%, indicating that the sequencing coverage was very high. The total number of OTUs differed between samples and types of biomass. The mean number of OTUs was 1614 ± 141 for biofilm and 993 ± 109 for activated sludge. The Chao1 index was used to evaluate community richness, i.e., the number of species in the biofilm and activated sludge communities, and the Shannon index was used to measure community diversity, taking into account both the abundance and the evenness of the species. The mean values of these indices indicated that the biofilm community was richer and more diverse than the activated sludge community (Chao1: 1734.64 ± 138.59 vs. 1105.72 ± 138.59; Shannon: 5.34 ± 0.23 vs. 4.27 ± 0.41). The differences between communities were all statistically significant (P < 0.05). Figure 4 shows the results of beta diversity analysis based on the Bray-Curtis dissimilarity. Principal Coordinates Analysis showed that the biofilm and activated sludge samples grouped into two separate clusters, although the distances between individual samples were quite large. Hierarchical analysis indicated the development of biofilm and activated sludge was independent and confirmed the distance of the differences between these two types of biomass. www.nature.com/scientificreports/ To model interactions and relationships between bacteria in the biofilm and activated sludge, co-occurrence network analysis was used. In the present study, two networks were created that represent the co-occurrence of genera in the biofilm and activated sludge. In Figs. 5 and 6, the color of each node is based on its modularity class parameter, and its size is based on its betweenness centrality. The basic parameters characterizing both networks are presented in Table S1. In general, the biofilm network had more connections between nodes than the activated sludge network, and the distance between nodes was smaller in the biofilm network, indicating that the microorganisms creating the biofilm are more closely related and have more relationships between them. Both networks had the same number of nodes (83), but the biofilm network had more edges (connectors between nodes symbolizing co-occurrence). In both networks, the number of positive associations was slightly higher than that of negative associations, accounting for 55% of the total number of connections. The mean clustering coefficient (i.e., the ratio between the observed and the maximum possible number of links between a node and its neighbors) was higher for the biofilm than for the activated sludge (0.556 vs. 0.432). Similarly, the network density, which is the ratio between the observed number edges and the maximum possible number of them, was higher for the biofilm (0.073 vs. 0.05). The network diameter (the distance between the two most distant nodes) was shorter for the biofilm than for the biomass (6 vs. 7). Likewise, the average path length, which is the number of edges in the shortest path between pair of nodes, was shorter in the biofilm network than in the activated sludge network (1.984 vs. 2.241). The mean node degree (the number of edges between one node and other nodes in the network) was greater in the biofilm network than in the activated sludge network (6.012 vs. 4.12). Node degree ranged from 1 to 31 in the biofilm network and from 1 to 23 in the activated sludge network. In the biofilm network there were four nodes with the highest degrees (≥ 30) that can be considered hub nodes: Diaphorobacter, Rhizorapis, Mesorhizobium, and Pseudoxanthomonas. These microorganisms had 61.5% positive and 38.5% negative connections with other microorganisms. Interestingly, although the abundance of Mesorhizobium and Pseudoxanthomonas was low (not exceeding 1.5% of the total bacterial community in any sample) they had positive associations with highly abundant bacteria, e.g., Ornithinibacter. The activated sludge network also had 4 hub nodes (with node degree ≥ 20): Nocardioides, Gemmatimonas, Leptothrix and Rhizorhapis. These hub nodes were connected to other nodes by similar amounts of positive and negative edges (51.8% and 48.2%, respectively). The size of the nodes in the created networks is proportional to their betweenness centrality (a parameter that indicates the frequency of occurrence of a particular node on the paths between two other nodes). High values of betweenness centrality indicate that a node has a central location in a network, while low values www.nature.com/scientificreports/ indicate that it has a peripheral location 13 . Microorganisms with high betweenness centrality play key roles and act like bridges between other bacteria in the network. In the biofilm network, Paracoccus, Phaeodactylibacter, and Pseudoxanthomonas had the highest values of betweenness centrality, whereas in the activated sludge network, Dongia, Diaphorobacter, and Rhizorhapis had the highest values. The networks were constructed with additional nodes representing the efficiency of pollutant removal processes, i.e., removal of organic and phosphorus compounds, as well as denitrification, ammonification, and nitrification. In the biofilm network, the efficiencies of phosphorus compound removal and of nitrification had    The efficiency of phosphorus removal was positively associated with the abundance of Candidatus Accumulibacter, Dechlorosoma, Thauera, and uncultured Saccharimonadales, while nitrification was positively associated with the abundance of taxa such as Nitrosomonas, Sphingomonas, and Thermomonas. The remaining efficiencies of pollutant removal had no or only 1-2 connections with microbial nodes. In the activated sludge network, in contrast, all efficiency nodes were connected with those of microbes, with degree ranging from 2 to 9. The nitrification and ammonification efficiency nodes had the highest degree and were positively associated with, for example, Nocardioides, Rhodobacter, and Sphingomonas. Organic compound removal efficiency was positively associated with Blastocatella, Ornithinibacter, and Terrimonas, whereas in the biofilm network, it had no edges.

Discussion
The biodiversity of both forms of biomass was examined using the Chao1 and Shannon indices. In all samples, the values of both these indices were significantly higher in the biofilm. This finding is consistent with the results obtained by Dong et al. 14 with a similar reactor; however, Jo et al. 12 obtained contrary results and did not observe similar differences. They speculated that this lack of differences might be caused by the fact that both flocks and biofilm result from microbial aggregation. On the other hand, biofilms have a layered structure and the conditions in each layer are a bit different, which is optimal for development of different groups of bacteria. This might be the reason why, in the present study, the biofilm community was richer and more diverse than that of the activated sludge. Various aeration strategies were applied during the experiment, which influenced the growth of the microorganisms in the reactor. Apparently, the change in aeration strategy had a stronger effect on the activated sludge bacterial community than on the biofilm community. In PCoA analysis, the biofilm samples, except for the first sample, were clustered more tightly than the activated sludge samples. This finding indicates that the biofilm community is more resistant to changing environmental conditions than the activated sludge community. This resistance to change can be explained by the fact that biofilm is a complex bacterial structure and is surrounded by a layer of extracellular polymeric substances (EPS) produced by the microbes, which acts as a physical barrier protecting the bacterial cells from environmental stress.
In both types of biomass, Proteobacteria and Bacteroidota were the most abundant phyla. Proteobacteria are typically the most abundant group in bacterial communities in municipal wastewater treatment systems. This taxon has many subgroups, the most common of which is Betaproteobacteria, which are largely responsible for removal of organic matter and nutrients 15 . The next most abundant phyla in the biofilm were Chloroflexi, Actinobacteriota and Acidobacteriota. Chloroflexi are filamentous bacteria that favor the formation of microbiological www.nature.com/scientificreports/ structures. Their filaments protrude from flocs and biofilm, which probably gives them better access to substrates in the surrounding liquid 9 and is believed to provide scaffolds on which activated sludge flocs form 16,17 . Actinobacteriota, as well as Proteobacteria and Bacteroidota, have been reported to be some of the core phyla that are widespread in wastewater treatment systems 18,19 . Acidobacteriota are involved in P-removal and have the potential to utilize various organic compounds, including glucose, xylose, acetate, and fatty acids 20 . After Proteobacteria and Bacteroidota, the next most abundant phyla in the activated sludge were Actinobacteriota and Patescibacteria (especially the uncultured Saccharimonadales). Patescibacteria have a small cell size and a reduced genome, which suggests that they are host-dependent syntrophs or parasitic 21 . Their small cell size might be advantageous in oligotrophic environments, due to their correspondingly increased surface-to-volume ratio 22 . The genus Zooglea was more abundant in activated sludge than in biofilm. Zooglea produce exopolysaccharides that participate in the formation of activated sludge flocs 23 . Network analysis revealed that positive associations between the abundance of taxa and the efficiency of nitrogen and phosphorus compound removal were more common in the biofilm community than in the activated sludge community. This may indicate that biofilm is a better environment for the growth of bacteria capable of decomposing these compounds. This hypothesis is also supported by the high abundance of certain groups of bacteria in the biofilm. For example, both the phylum Nitrospirota and the genus Nitrospira were significantly more abundant in the biofilm than in the activated sludge. Nitrospirota includes bacteria that oxidize nitrites to nitrates (NOB), as well as the recently discovered comammox bacteria (complete ammonia oxidizers), which can perform both steps of the nitrification process 24,25 . It could suggest that in the biofilm the commamox process could be important in ammonia oxidation. Nitrosomonas, a representative of ammonia oxidizing bacteria (AOB), was also more abundant in the biofilm, but it was not as numerous as Nitrospira. The results of this study are similar to those of Shao et al. 26 , who compared attached biofilm and activated sludge flocs in an Integrated Fixed-Film Activated Sludge-Sequencing Batch Biofilm Reactor (IFAS-SBR). Nitrosomonas had also negative association with anammox bacterium Candidatus Brocadia, which may be caused by the competition for the same substrate. Probably Candidatus Brocadia was abundant in deeper layers of a biofilm, where concentration of oxygen is low. Hosokawa et al. 27 reported that anammox bacteria occur together with Patescibacteria, however in our study this taxa was more abundant in the activated sludge. Perhaps this is an example of the cooperation of biofilm with activated sludge in a hybrid system. In the biofilm, denitrifiers were highly abundant, although they were also quite common in the activated sludge. From this group of microorganisms, Denitratisoma, Rhodobacter and Dechlorosoma were present in the biofilm, while Rhodobacter and Thaurea were present in the activated sludge.
Bai et al. 28 observed similar results and concluded that biofilm is a better environment than activated sludge for the development of Denitratisoma, Rhodobacter and Dechlorosoma, due to the longer solid retention time associated with biofilm. Interestingly, those authors also observed that there were more phosphorus accumulating organisms (PAO) in the activated sludge. This differs from the results of the present study, in which, for example, C. Accumulibacter, a PAO, was more abundant in the biofilm than in the activated sludge flocs. McIlroy et al. 29 reported that Candidatus Competibacter was also quite numerous in biofilm samples; this is a glycogenaccumulating organism that is believed to compete for resources with PAOs. Although associations between bacteria and efficiency of different pollutant removal were observed, there is no clear connection between bacteria and concentration of nitrogen forms. For instance, since Nitrosomonas oxidize ammonia, it can be expected that in the network, abundance of Nitrosomonas will be inversely proportional to the ammonia concentration and directly proportional to the nitrates concentration. However, such obvious dependencies did not occur in the analyzed network. The reason for this may be that in the tested system, different processes, carried out by many different groups of microorganisms, and affecting the concentration of individual forms of nitrogen, take place. Because many different groups of bacteria cooperate with each other in removing nitrogen compounds from wastewater, creating a kind of functional whole, it is difficult to determine the contribution of individual units in this process.
Although the number of nodes was similar in the biofilm and activated sludge networks, the number of interactions between nodes was greater in the biofilm network. These results indicate that the genera present in the biofilm interact with each other to a greater degree than those in the activated sludge. The correlations in the abundance of taxa were predominately positive in both types of biomass. A greater number of positive than negative correlations was also noted by Jo et al. 12 in their research, but the number of positive associations in their study (92% in the biofilm network and 75% in the activated sludge network) was much higher than in ours (55%). Based on their betweenness centrality, key taxa were identified in both networks. Microorganisms with high betweenness centrality often lie on the shortest path between two other nodes, and for this reason, they are considered important for the flow of information between community members 30 . One of these key taxa in the biofilm network was Paracoccus, which is a heterotrophic nitrifying and aerobic denitrifying bacterium that can also remove phosphorus 31 . Other groups of bacteria with high betweenness centrality were Pseudoxanthomonas (capable of removing nitrogen and phosphorous under aerobic conditions 32 and Phaeodactylibacter (a denitrifier 33 ). In the activated sludge network Dongia and Diaphorobacter had the highest betweenness centrality; these taxa are involved in the transformation of nitrogen compounds 27,34 . The differences between the biofilm and the activated sludge network also indicate that other bacteria may be responsible for the transformation of nitrogen compounds in these two environments. Network analysis and the high values of betweenness centrality of the mentioned above taxa suggest that even taxa with a low abundance might play important roles in the bacterial community. The role of such bacteria in wastewater treatment processes is not well known and requires further investigation.

Conclusions
This study compared the structures of the microbial communities in biofilm and activated sludge from an IFAS-MBSBBR hybrid reactor. The bacterial composition of the biofilm differed from that of the activated sludge, although some core groups of bacteria (Proteobacteria, Bacteroidota, Actinobacteriota and Acidobacteriota) were highly abundant in both types of biomass. The biofilm was more diverse in terms of bacterial composition and a better environment for the development of nitrifiers (e.g., Nitrospira, Nitrosomonas) and phosphorus accumulating organisms (C. Accumulibacter). The bacteria in the biofilm network had more connections with each other than those in the activated sludge network. Furthermore, in the biofilm network, more bacteria were connected with nitrogen and phosphorus removal efficiency, which indicates that biofilm might play a larger role in the removal of these pollutants. Network analysis also revealed that even bacteria with low abundance might play important roles in the community, although these roles require further investigation. A better understanding of the contribution of these taxa will provide a more complete picture of these complex communities and the relationships between the bacteria that create them. The reactor operated in a system of 3 eight-hour cycles per day. A single treatment cycle involved the following phases: I phase without aeration with wastewater dosing (50 min), I phase with aeration (190 min), II phase without aeration with wastewater dosing (30 min), II aerobic phase (150 min), sedimentation (50 min), decantation (10 min). During 573 days of the experiment duration, periods with blower unit turned off were introduced in aerobic phases to obtain an intermittent aeration strategy, or oxygen concentration was changed. Moreover, between the 447 and 547th research day, the contamination load of the reactor was decreased through a decrease in wastewater dose from 10 L/d to 6.6 L/d ( Table 2). Samples of infow and outfow water were collected between December 2018 and June 2020. Chemical analysis was carried out according to standard methods (Table_S2).  Sequencing data analysis. Raw paired-end sequences were processed using the QIIMEII 36 software package. Paired-end sequences were merged using the fast-join algorithm. Reads that software could not merge were excluded from further analyses. The filtering process quality score (q < 20) was obtained using the Cutadapt algorithm 37 . Chimeric sequences were detected and excluded from analyses using USEARCH 38 .16S rRNA OTUs were picked from the Illumina reads using a closed-reference OTU picking protocol against the SILVA_V_138 database 39 . Sequences were clustered at 97% identity and trimmed to span only the 16S rRNA V4 region flanked by the sequencing primers. Taxonomy assignments were associated with OTUs based on the taxonomy associated with the SILVA_V_138 reference sequence defining each OTU.

Materials and methods
Statistical analysis. Statistical comparison of biofilm and activated sludge samples was made using STAMP software (Statistical Analysis of Metagenomics Profiles (http:// kiwi. cs. dal. ca/ Softw are/ STAMP)) 40 . Significance was determined with White's non-parametric t-test. Results with P < 0.05 were considered significant. Bacteria co-occurrence networks were created based on a correlation analysis of the taxonomic profiles 41 . For the analysis, the most abundant taxa in the studied metagenomes were selected. Spearman's correlation analysis (with a significance threshold of α = 0.05) was carried out using STATISTICA v.13.1 (StatSoft, Inc, Tulsa, OK, USA). Networks were plotted only for strongly correlated taxa (with correlation coefficients higher than 0.75 or lower than -0.75). The correlation matrices obtained in this way were used to create networks using Gephi software 42 . Alpha and beta diversity analysis were performed with the use of EZBioCloud platform 43 .

Data availability
The sequences reads were deposited in the NCBI Sequence Read Archive (SRA) under the accession number PRJNA793374.